The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …

Deep neural networks have been well-known for their superb handling of various machine
learning and artificial intelligence tasks. However, due to their over-parameterized black-box …

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

When we are faced with challenging image classification tasks, we often explain our
reasoning by dissecting the image, and pointing out prototypical aspects of one class or …

In 2018, a landmark challenge in artificial intelligence (AI) took place, namely, the
Explainable Machine Learning Challenge. The goal of the competition was to create a …

Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to
explain the predictions made by GNNs. Existing explanation methods mainly focus on post …

Time series data is increasingly used in a wide range of fields, and it is often relied on in
crucial applications and high-stakes decision-making. For instance, sensors generate time …

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes
dramatically challenging to explain AI models in the past decade.“Explanation” has a flexible …